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Artificial Intelligence and Deep Learning in Pathology
- 1st Edition - June 2, 2020
- Editor: Stanley Cohen
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 6 7 5 3 8 - 3
Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern re… Read more
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Request a sales quoteRecent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, with a team of experts, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience.
- Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible.
- Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning.
- Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.
Surgical pathologists in training and in practice
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Contributors
- Preface
- Chapter 1. The evolution of machine learning: past, present, and future
- Introduction
- Rules-based versus machine learning: a deeper look
- Varieties of machine learning
- General aspects of machine learning
- Deep learning and neural networks
- The role of AI in pathology
- Chapter 2. The basics of machine learning: strategies and techniques
- Introduction
- Shallow learning
- The curse of dimensionality and principal component analysis
- Deep learning and the artificial neural network
- Overfitting and underfitting
- Things to come
- Chapter 3. Overview of advanced neural network architectures
- Introduction
- Network depth and residual connections
- Autoencoders and unsupervised pretraining
- Transfer learning
- Generative models and generative adversarial networks
- Recurrent neural networks
- Reinforcement learning
- Ensembles
- Genetic algorithms
- Chapter 4. Complexity in the use of artificial intelligence in anatomic pathology
- Introduction
- Life before machine learning
- Multilabel classification
- Multiple objects
- Advances in multilabel classification
- Graphical neural networks
- Weakly supervised learning
- Synthetic data
- N-shot learning
- One-class learning
- General considerations
- Summary and conclusions
- Chapter 5. Dealing with data: strategies of preprocessing data
- Introduction
- Overview of preprocessing
- Feature selection, extraction, and correction
- Feature transformation, standardization, and normalization
- Feature engineering
- Mathematical approaches to dimensional reduction
- Dimensional reduction in deep learning
- Imperfect class separation in the training set
- Fairness and bias in machine learning
- Summary
- Chapter 6. Digital pathology as a platform for primary diagnosis and augmentation via deep learning
- Introduction
- Digital imaging in pathology
- Telepathology
- Whole slide imaging
- Whole slide image viewers
- Whole slide image data and workflow management
- Selection criteria for a whole slide scanner
- Evolution of whole slide imaging systems
- Infrastructure requirements and checklist for rolling out high-throughput whole slide imaging workflow solution
- Whole slide imaging and primary diagnosis
- Whole slide imaging and image analysis
- Whole slide imaging and deep learning
- Conclusions
- Chapter 7. Applications of artificial intelligence for image enhancement in pathology
- Introduction
- Common machine learning tasks
- Commonly used deep learning methodologies
- Common training and testing practices
- Deep learning for microscopy enhancement in histopathology
- Deep learning for computationally aided diagnosis in histopathology
- Future prospects
- Chapter 8. Precision medicine in digital pathology via image analysis and machine learning
- Introduction
- Applications of image analysis and machine learning
- Practical concepts and theory of machine learning
- Image-based digital pathology
- Regulatory concerns and considerations
- Chapter 9. Artificial intelligence methods for predictive image-based grading of human cancers
- Introduction
- Tissue preparation and staining
- Image acquisition
- Stain normalization
- Unmixing of immunofluorescence spectral images
- Automated detection of tumor regions in whole-slide images
- Image segmentation
- Protein biomarker features
- Morphological features for cancer grading and prognosis
- Modeling
- Ground truth data for AI-based features
- Conclusion
- Chapter 10. Artificial intelligence and the interplay between tumor and immunity
- Introduction
- Immune surveillance and immunotherapy
- Identifying TILs with deep learning
- Multiplex immunohistochemistry with digital pathology and deep learning
- Vendor platforms
- Conclusion
- Chapter 11. Overview of the role of artificial intelligence in pathology: the computer as a pathology digital assistant
- Introduction
- Computational pathology: background and philosophy
- Machine learning tools in computational pathology: types of artificial intelligence
- The need for human intelligence–artificial intelligence partnerships
- Human transparent machine learning approaches
- Image-based computational pathology
- First fruits of computational pathology: the evolving digital assistant
- Artificial intelligence and regulatory challenges
- Educating machines–educating us: learning how to learn with machines
- Index
- No. of pages: 288
- Language: English
- Edition: 1
- Published: June 2, 2020
- Imprint: Elsevier
- Paperback ISBN: 9780323675383
SC
Stanley Cohen
Dr. Cohen is currently interested in integrating computational imaging with digital workflows. He previously served as President of the American Society for Investigative Pathology (ASIP) and Treasurer and Member of the Executive Board of FASEB. Science-related activities also include chairmanships of study sections for the NIH and DOD and membership on multiple editorial boards. He is currently the Associate Editor for digital and computational pathology and artificial intelligence topic category for the American Journal of Pathology. He is a Senior Fellow of the Association of Pathology Chairs and Co-Chair of the ASIP Special Interest Group on Digital and Computational Pathology. Awards include the Gold-Headed Cane (ASIP) and the Golden Goose Award (AAAS). He is a member of the Digital Pathology Association (DPA), the Board of the International Academy of Digital Pathology (IADP), and Chair of the External Advisory Board of the Alpert Foundation.
Affiliations and expertise
PhD, MD